def opt_run(): param_grid = [{'C': [1, 10, 100, 1000]}] clf = linear_model.LogisticRegression() grid_search = GridSearchCV(clf, cv=16, param_grid=param_grid) XX, Y = sensors_data.read_data(0) grid_search.fit(XX, Y) report(grid_search)
def run(): if not os.path.isdir(config.model_logreg_folder): os.mkdir(config.model_logreg_folder) for i in range(120): XX, Y = sensors_data.read_data(i) clf = linear_model.LogisticRegression() scores = cross_validation.cross_val_score(clf, XX, Y, cv=16) save_model(clf, 'c1', i) print str(i) + '\t' + str(scores.mean()) + '\t' + str(scores.std()) sys.stdout.flush()